3 Steps To Big Data Leadership

I saw a poster the other day that read, “Don’t look back, you’re not going that way.” It’s good advice for life in general, but also for business. Companies today no longer need to constantly look in their rear-view mirror to try and piece together what’s coming. Big data is enabling them to take a forward-looking view of the business, bringing with it the potential to make meaningful changes to help them run simple.

In a recent study of Big Data innovators, IDC found that European organizations are starting to see real value from Big Data and analytics, with 72 percent seeing an ROI in less than twelve months. In fact, the UK and Germany are the furthest ahead when it comes to incorporating technology-led transformation as an essential part of their strategy.

But not everyone moves at the same pace. IDC recently created a Big Data maturity model to help organizations understand where they are in the five stages of Big Data maturity: ad hoc, opportunistic, repeatable, managed, and optimized. (The research is well worth a read if you’re interested in finding out where your organization is on this scale). Clearly, we’d all love to be at the optimized stage, so I thought I’d share the three key recommendations IDC discovered in its research profiling Big Data leaders.

First, ensure you have a very clear desired outcome and business case for your Big Data project. Agreement at the outset will help frame all your decisions moving forward. It also ensures you don’t surprise any lines of business or departments along the way! It’s important to consolidate and coordinate budget across the business for Big Data projects, while keeping a degree of flexibility for specific ad-hoc ones. With funding in place, each new Big Data project requires its own business case with defined outcomes around revenue, cost reduction, risk mitigation, or other relevant metrics to determine levels of investment against ROI.

Second, put a dynamic Big Data strategy in place. By this I mean treat the strategy as a fluid and transparent concept with continuous updates and input from relevant stakeholders, including IT, analytics teams, business executives, and users across the organization. Leverage best practices from a leading department or business unit so they can be replicated into new areas. IT should ensure that the right governance model and integration capabilities are put in place from the outset. It’s also important to make sure that the strategy addresses key considerations around your project – namely intent, data, people, process, and technology – and most importantly, it must have C-Level support and sign-off.

Finally, set up a Big Data competency center that includes stakeholders from IT, business, and analytics functions. Ideally, this should sit in the business under the COO or CEO if appropriate, and should bring together all components of a Big Data strategy, including stakeholders, technology architecture, analytical skills, and vendor and service management. This not only helps raise the profile of Big Data projects internally, but should also facilitate with setting goals around moving the organization to new levels of maturity and readiness.

When implemented well, Big Data is taking organizations to new levels of business transformation. By understanding where your organization is on this Big Data maturity model, you can gain greater insight into your company’s own journey.